Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning models usually consider data fusion under fixed and compulsory collaborative relations among agents, which is not as flexible and autonomous as human collaboration. To fill this gap, we propose a distributed multi-agent learning model inspired by human collaboration, in which the agents can autonomously detect suitable collaborators and refer to collaborators' model for better performance. To implement such adaptive collaboration, we use a collaboration graph to indicate the pairwise collaborative relation. The collaboration graph can be obtained by graph learning techniques based on model similarity between different agents. Since model similarity can not be formulated by a fixed graphical optimization, we design a graph learning network by unrolling, which can learn underlying similar features among potential collaborators. By testing on both regression and classification tasks, we validate that our proposed collaboration model can figure out accurate collaborative relationship and greatly improve agents' learning performance.

翻译：多智能体学习因能解决数据交换受限下的分布式机器学习场景而日益受到关注。然而，现有模型通常基于固定且强制性的协作关系进行数据融合，缺乏人类协作的灵活性与自主性。针对这一不足，本文提出了一种受人类协作启发的分布式多智能体学习模型：智能体可自主识别适宜协作者，并参考其模型以提升性能。为实现这种自适应协作，我们采用协作图表征两两协作关系，并通过基于不同智能体模型相似性的图学习技术获取该图。由于模型相似性无法通过固定图优化问题描述，我们设计了基于展开方式的图学习网络，该网络能学习潜在协作者间的隐含相似特征。在回归与分类任务上的实验表明，所提协作模型可准确识别协作关系，并大幅提升智能体的学习性能。